Influence of maneuverability on helicopter combat effectiveness

A computational procedure employing a stochastic learning method in conjunction with dynamic simulation of helicopter flight and weapon system operation was used to derive helicopter maneuvering strategies. The derived strategies maximize either survival or kill probability and are in the form of a feedback control based upon threat visual or warning system cues. Maneuverability parameters implicit in the strategy development include maximum longitudinal acceleration and deceleration, maximum sustained and transient load factor turn rate at forward speed, and maximum pedal turn rate and lateral acceleration at hover. Results are presented in terms of probability of skill for all combat initial conditions for two threat categories

Dynamic Surface and Active Disturbance Rejection Control for Path Following of an Underactuated UUV

This paper addresses the problem of accurate path following control for an underactuated unmanned underwater vehicle (UUV) in the horizontal plane. For an underactuated UUV, the line-of-sight (LOS) guidance method is adopted to map 2D reference trajectory into a desired orientation, and through the tracking of heading to achieve path following, where the sideslip is introduced to modify the desired orientation. In this paper, we propose a method called dynamic surface and active disturbance rejection control (DS-ADRC) to solve the path following control problem. This controller can effectively avoid the phenomenon of explosion of terms in the conventional backstepping method, reduce the dependence on the UUV controller mathematical model, and enhance the antijamming ability. Simulation is carried out to verify the effectiveness of the proposed control method for an underactuated UUV. The results show that, even for this controller with disturbance, the cross-track error of UUV is gradually converged to zero and has some certain robustness

해양 작업 지원선의 자율 운항 및 설치 작업 지원을 위한 시뮬레이션 방법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 조선해양공학과, 2019. 2. 노명일.Autonomous ships have gained a huge amount of interest in recent years, like their counterparts on land{autonomous cars, because of their potential to significantly lower the cost of operation, attract seagoing professionals and increase transportation safety. Technologies developed for the autonomous ships have potential to notably reduce maritime accidents where 75% cases can be attributed to human error and a significant proportion of these are caused by fatigue and attention deficit. However, developing a high-level autonomous system which can operate in an unstructured and unpredictable environment is still a challenging task. When the autonomous ships are operating in the congested waterway with other manned or unmanned vessels, the collision avoidance algorithm is the crucial point in keeping the safety of both the own ship and any encountered ships. Instead of developing new traffic rules for the autonomous ships to avoid collisions with each other, autonomous ships are expected to follow the existing guidelines based on the International Regulations for Preventing Collisions at Sea (COLREGs). Furthermore, when using the crane on the autonomous ship to transfer and install subsea equipment to the seabed, the heave and swaying phenomenon of the subsea equipment at the end of flexible wire ropes makes its positioning at an exact position is very difficult. As a result, an Anti-Motion Control (AMC) system for the crane is necessary to ensure the successful installation operation. The autonomous ship is highly relying on the effectiveness of autonomous systems such as autonomous path following system, collision avoidance system, crane control system and so on. During the previous two decades, considerable attention has been paid to develop robust autonomous systems. However, several are facing challenges and it is worthwhile devoting much effort to this. First of all, the development and testing of the proposed control algorithms should be adapted across a variety of environmental conditions including wave, wind, and current. This is one of the challenges of this work aimed at creating an autonomous path following and collision avoidance system in the ship. Secondly, the collision avoidance system has to comply with the regulations and rules in developing an autonomous ship. Thirdly, AMC system with anti-sway abilities for a knuckle boom crane remains problems regarding its under-actuated mechanism. At last, the performance of the control system should be evaluated in advance of the operation to perform its function successfully. In particular, such performance analysis is often very costly and time-consuming, and realistic conditions are typically impossible to establish in a testing environment. Consequently, to address these issues, we proposed a simulation framework with the following scenarios, which including the autonomous navigation scenario and crane operation scenario. The research object of this study is an autonomous offshore support vessel (OSV), which provides support services to offshore oil and gas field development such as offshore drilling, pipe laying, and oil producing assets (production platforms and FPSOs) utilized in EP (Exploration Production) activities. Assume that the autonomous OSV confronts an urgent mission under the harsh environmental conditions: on the way to an imperative offshore construction site, the autonomous OSV has to avoid target ships while following a predefined path. When arriving at the construction site, it starts to install a piece of subsea equipment on the seabed. So what technologies are needed, what should be invested for ensuring the autonomous OSV could robustly kilometers from shore, and how can an autonomous OSV be made at least as safe as the conventional ship. In this dissertation, we focus on the above critical activities for answering the above questions. In the general context of the autonomous navigation and crane control problem, the objective of this dissertation is thus fivefold: • Developing a COLREGs-compliant collision avoidance system. • Building a robust path following and collision avoidance system which can handle the unknown and complicated environment. • Investigating an efficient multi-ship collision avoidance method enable it easy to extend. • Proposing a hardware-in-the-loop simulation environment for the AHC system. • Solving the anti-sway problem of the knuckle boom crane on an autonomous OSV. First of all, we propose a novel deep reinforcement learning (RL) algorithm to achieve effective and efficient capabilities of the path following and collision avoidance system. To perform and verify the proposed algorithm, we conducted simulations for an autonomous ship under unknown environmental disturbance iiito adjust its heading in real-time. A three-degree-of-freedom dynamic model of the autonomous ship was developed, and the Line-of-sight (LOS) guidance system was used to converge the autonomous ship to follow the predefined path. Then, a proximal policy optimization (PPO) algorithm was implemented on the problem. By applying the advanced deep RL method, in which the autonomous OSV learns the best behavior through repeated trials to determine a safe and economical avoidance behavior in various circumstances. The simulation results showed that the proposed algorithm has the capabilities to guarantee collision avoidance of moving encountered ships while ensuring following a predefined path. Also, the algorithm demonstrated that it could manage complex scenarios with various encountered ships in compliance with COLREGs and have the excellent adaptability to the unknown, sophisticated environment. Next, the AMC system includes Anti-Heave Control (AHC) and Anti-Sway Control (ASC), which is applied to install subsea equipment in regular and irregular for performance analysis. We used the proportional-integral-derivative (PID) control method and the sliding mode control method respectively to achieve the control objective. The simulation results show that heave and sway motion could be significantly reduced by the proposed control methods during the construction. Moreover, to evaluate the proposed control system, we have constructed the HILS environment for the AHC system, then conducted a performance analysis of it. The simulation results show the AHC system could be evaluated effectively within the HILS environment. We can conclude that the proposed or adopted methods solve the problems issued in autonomous system design.해양 작업 지원선 (Offshore Support Vessel: OSV)의 경우 극한의 환경에도 불구하고 출항하여 해상에서 작업을 수행해야 하는 경우가 있다. 이러한 위험에의 노출을 최소화하기 위해 자율 운항에 대한 요구가 증가하고 있다. 여기서의 자율 운항은 선박이 출발지에서 목적지까지 사람의 도움 없이 이동함을 의미한다. 자율 운항 방법은 경로 추종 방법과 충돌 회피 방법을 포함한다. 우선, 운항 및 작업 중 환경 하중 (바람, 파도, 조류 등)에 대한 고려를 해야 하고, 국제 해상 충돌 예방 규칙 (Convention of the International Regulations for Preventing Collisions at Sea, COLREGs)에 의한 선박간의 항법 규정을 고려하여 충돌 회피 규칙을 준수해야 한다. 특히 연근해의 복잡한 해역에서는 많은 선박을 자동으로 회피할 필요가 있다. 기존의 해석적인 방법을 사용하기 위해서는 선박들에 대한 정확한 시스템 모델링이 되어야 하며, 그 과정에서 경험 (experience)에 의존하는 파라미터 튜닝이 필수적이다. 또한, 회피해야 할 선박 수가 많아질 경우 시스템 모델이 커지게 되고 계산 양과 계산 시간이 늘어나 실시간 적용이 어렵다는 단점이 있다. 또한, 경로 추종 및 충돌 회피를 포함하여 자율 운항 방법을 적용하기가 어렵다. 따라서 본 연구에서는 강화 학습 (Reinforcement Learning: RL) 기법을 이용하여 기존 해석적인 방법의 문제점을 극복할 수 있는 방법을 제안하였다. 경로를 추종하는 선박 (agent)은 외부 환경 (environment)과 상호작용하면서 학습을 진행한다. State S_0 (선박의 움직임과 관련된 각종 상태) 가지는 agent는 policy (현재 위치에서 어떤 움직임을 선택할 것인가)에 따라 action A_0 (움직일 방향) 취한다. 이에 environment는 agent의 다음 state S_1 을 계산하고, 그에 따른 보상 R_0 (해당 움직임의 적합성)을 결정하여 agent에게 전달한다. 이러한 작업을 반복하면서 보상이 최대가 되도록 policy를 학습하게 된다. 한편, 해상에서 크레인을 이용한 장비의 이동이나 설치 작업 시 위험을 줄이기 위해 크레인의 거동 제어에 대한 요구가 증가하고 있다. 특히 해상에서는 선박의 운동에 의해 크레인에 매달린 물체가 상하 동요 (heave)와 크레인을 기준으로 좌우 동요 (sway)가 발생하는데, 이러한 운동은 작업을 지연시키고, 정확한 위치에 물체를 놓지 못하게 하며, 자칫 주변 구조물과의 충돌을 야기할 수 있다. 이와 같은 동요를 최소화하는 Anti-Motion Control (AMC) 시스템은 Anti-Heave Control (AHC)과 Anti-Sway Control (ASC)을 포함한다. 본 연구에서는 해양 작업 지원선에 적합한 AMC 시스템의 설계 및 검증 방법을 연구하였다. 먼저 상하 동요를 최소화하기 위해 크레인의 와이어 길이를 능동적으로 조정하는 AHC 시스템을 설계하였다. 또한, 기존의 제어 시스템의 검증 방법은 실제 선박이나 해양 구조물에 해당 제어 시스템을 직접 설치하기 전에는 그 성능을 테스트하기가 힘들었다. 이를 해결하기 위해 본 연구에서는 Hardware-In-the-Loop Simulation (HILS) 기법을 활용하여 AHC 시스템의 검증 방법을 연구하였다. 또한, ASC 시스템을 설계할 때 제어 대상이 under-actuated 시스템이기 때문에 제어하기가 매우 어렵다. 따라서 본 연구에서는 sliding mode control 알고리즘을 이용하며 다관절 크레인 (knuckle boom crane)의 관절 (joint) 각도를 제어하여 좌우 동요를 줄일 수 있는 ASC 시스템을 설계하였다.Chapter 1 Introduction 1 1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . 1 1.2 Requirements for Autonomous Operation . . . . . . . . . . . . . 5 1.2.1 Path Following for Autonomous Ship . . . . . . . . . . . . 5 1.2.2 Collision Avoidance for Autonomous Ship . . . . . . . . . 5 1.2.3 Anti-Motion Control System for Autonomous Ship . . . . 6 1.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.1 Related Work for Path Following System . . . . . . . . . 9 1.3.2 Related Work for Collision Avoidance System . . . . . . . 9 1.3.3 Related Work for Anti-Heave Control System . . . . . . . 13 1.3.4 Related Work for Anti-Sway Control System . . . . . . . 14 1.4 Configuration of Simulation Framework . . . . . . . . . . . . . . 16 1.4.1 Application Layer . . . . . . . . . . . . . . . . . . . . . . 16 1.4.2 Autonomous Ship Design Layer . . . . . . . . . . . . . . . 17 1.4.3 General Technique Layer . . . . . . . . . . . . . . . . . . 17 1.5 Contributions (Originality) . . . . . . . . . . . . . . . . . . . . . 19 Chapter 2 Theoretical Backgrounds 20 2.1 Maneuvering Model for Autonomous Ship . . . . . . . . . . . . . 20 2.1.1 Kinematic Equation for Autonomous Ship . . . . . . . . . 20 2.1.2 Kinetic Equation for Autonomous Ship . . . . . . . . . . 21 2.2 Multibody Dynamics Model for Knuckle Boom Crane of Autonomous Ship. . . 25 2.2.1 Embedding Techniques . . . . . . . . . . . . . . . . . . . . 25 2.3 Control System Design . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3.1 Proportional-Integral-Derivative (PID) Control . . . . . . 31 2.3.2 Sliding Mode Control . . . . . . . . . . . . . . . . . . . . 31 2.4 Deep Reinforcement Learning Algorithm . . . . . . . . . . . . . . 34 2.4.1 Value Based Learning Method . . . . . . . . . . . . . . . 36 2.4.2 Policy Based Learning Method . . . . . . . . . . . . . . . 37 2.4.3 Actor-Critic Method . . . . . . . . . . . . . . . . . . . . . 41 2.5 Hardware-in-the-Loop Simulation . . . . . . . . . . . . . . . . . . 43 2.5.1 Integrated Simulation Method . . . . . . . . . . . . . . . 43 Chapter 3 Path Following Method for Autonomous OSV 46 3.1 Guidance System . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.1.1 Line-of-sight Guidance System . . . . . . . . . . . . . . . 46 3.2 Deep Reinforcement Learning for Path Following System . . . . . 50 3.2.1 Deep Reinforcement Learning Setup . . . . . . . . . . . . 50 3.2.2 Neural Network Architecture . . . . . . . . . . . . . . . . 56 3.2.3 Training Process . . . . . . . . . . . . . . . . . . . . . . . 58 3.3 Implementation and Simulation Result . . . . . . . . . . . . . . . 62 3.3.1 Implementation for Path Following System . . . . . . . . 62 3.3.2 Simulation Result . . . . . . . . . . . . . . . . . . . . . . 65 3.4 Comparison Results . . . . . . . . . . . . . . . . . . . . . . . . . 83 3.4.1 Comparison Result of PPO with PID . . . . . . . . . . . 83 3.4.2 Comparison Result of PPO with Deep Q-Network (DQN) 87 Chapter 4 Collision Avoidance Method for Autonomous OSV 89 4.1 Deep Reinforcement Learning for Collision Avoidance System . . 89 4.1.1 Deep Reinforcement Learning Setup . . . . . . . . . . . . 89 4.1.2 Neural Network Architecture . . . . . . . . . . . . . . . . 93 4.1.3 Training Process . . . . . . . . . . . . . . . . . . . . . . . 94 4.2 Implementation and Simulation Result . . . . . . . . . . . . . . . 95 4.2.1 Implementation for Collision Avoidance System . . . . . . 95 4.2.2 Simulation Result . . . . . . . . . . . . . . . . . . . . . . 100 4.3 Implementation and Simulation Result for Multi-ship Collision Avoidance Method . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.3.1 Limitations of Multi-ship Collision Avoidance Method - 1 107 4.3.2 Limitations of Multi-ship Collision Avoidance Method - 2 108 4.3.3 Implementation of Multi-ship Collision Avoidance Method 110 4.3.4 Simulation Result of Multi-ship Collision Avoidance Method 118 Chapter 5 Anti-Motion Control Method for Knuckle Boom Crane 129 5.1 Configuration of HILS for Anti-Heave Control System . . . . . . 129 5.1.1 Virtual Mechanical System . . . . . . . . . . . . . . . . . 132 5.1.2 Virtual Sensor and Actuator . . . . . . . . . . . . . . . . 138 5.1.3 Control System Design . . . . . . . . . . . . . . . . . . . . 141 5.1.4 Integrated Simulation Interface . . . . . . . . . . . . . . . 142 5.2 Implementation and Simulation Result of HILS for Anti-Heave Control System . . . . . . . . 145 5.2.1 Implementation of HILS for Anti-Heave Control System . 145 5.2.2 Simulation Result of HILS for Anti-Heave Control System 146 5.3 Validation of HILS for Anti-Heave Control System . . . . . . . . 159 5.3.1 Hardware Setup . . . . . . . . . . . . . . . . . . . . . . . 159 5.3.2 Comparison Result . . . . . . . . . . . . . . . . . . . . . . 161 5.4 Configuration of Anti-Sway Control System . . . . . . . . . . . . 162 5.4.1 Mechanical System for Knuckle Boom Crane . . . . . . . 162 5.4.2 Anti-Sway Control System Design . . . . . . . . . . . . . 165 5.4.3 Implementation and Simulation Result of Anti-Sway Control . . . . . . . . . . . . . . 168 Chapter 6 Conclusions and Future Works 176 Bibliography 178 Chapter A Appendix 186 국문초록 188Docto

An adaptive autopilot design for an uninhabited surface vehicle

An adaptive autopilot design for an uninhabited surface vehicle Andy SK Annamalai The work described herein concerns the development of an innovative approach to the design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of autonomous missions, uninhabited surface vehicles must be able to operate with a minimum of external intervention. Existing strategies are limited by their dependence on a fixed model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect on performance. This thesis presents an approach based on an adaptive model predictive control that is capable of retaining full functionality even in the face of sudden changes in dynamics. In the first part of this work recent developments in the field of uninhabited surface vehicles and trends in marine control are discussed. Historical developments and different strategies for model predictive control as applicable to surface vehicles are also explored. This thesis also presents innovative work done to improve the hardware on existing Springer uninhabited surface vehicle to serve as an effective test and research platform. Advanced controllers such as a model predictive controller are reliant on the accuracy of the model to accomplish the missions successfully. Hence, different techniques to obtain the model of Springer are investigated. Data obtained from experiments at Roadford Reservoir, United Kingdom are utilised to derive a generalised model of Springer by employing an innovative hybrid modelling technique that incorporates the different forward speeds and variable payload on-board the vehicle. Waypoint line of sight guidance provides the reference trajectory essential to complete missions successfully. The performances of traditional autopilots such as proportional integral and derivative controllers when applied to Springer are analysed. Autopilots based on modern controllers such as linear quadratic Gaussian and its innovative variants are integrated with the navigation and guidance systems on-board Springer. The modified linear quadratic Gaussian is obtained by combining various state estimators based on the Interval Kalman filter and the weighted Interval Kalman filter. Change in system dynamics is a challenge faced by uninhabited surface vehicles that result in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms are analysed and an innovative, adaptive autopilot based on model predictive control is designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that is obtained by combining the advances made to weighted least squares during this research and is used in conjunction with model predictive control. Successful experimentation is undertaken to validate the performance and autonomous mission capabilities of the adaptive autopilot despite change in system dynamics.EPSRC (Engineering and Physical Sciences Research Council

Advanced Techniques for Design and Manufacturing in Marine Engineering

Modern engineering design processes are driven by the extensive use of numerical simulations; naval architecture and ocean engineering are no exception. Computational power has been improved over the last few decades; therefore, the integration of different tools such as CAD, FEM, CFD, and CAM has enabled complex modeling and manufacturing problems to be solved in a more feasible way. Classical naval design methodology can take advantage of this integration, giving rise to more robust designs in terms of shape, structural and hydrodynamic performances, and the manufacturing process.This Special Issue invites researchers and engineers from both academia and the industry to publish the latest progress in design and manufacturing techniques in marine engineering and to debate the current issues and future perspectives in this research area. Suitable topics for this issue include, but are not limited to, the following:CAD-based approaches for designing the hull and appendages of sailing and engine-powered boats and comparisons with traditional techniques;Finite element method applications to predict the structural performance of the whole boat or of a portion of it, with particular attention to the modeling of the material used;Embedded measurement systems for structural health monitoring;Determination of hydrodynamic efficiency using experimental, numerical, or semi-empiric methods for displacement and planning hulls;Topology optimization techniques to overcome traditional scantling criteria based on international standards;Applications of additive manufacturing to derive innovative shapes for internal reinforcements or sandwich hull structures

Helicopter Handling Qualities

Helicopters are used by the military and civilian communities for a variety of tasks and must be capable of operating in poor weather conditions and at night. Accompanying extended helicopter operations is a significant increase in pilot workload and a need for better handling qualities. An overview of the status and problems in the development and specification of helicopter handling-qualities criteria is presented. Topics for future research efforts by government and industry are highlighted

Cooperative Control and Fault Recovery for Network of Heterogeneous Autonomous Underwater Vehicles

The purpose of this thesis is to develop cooperative recovery control schemes for a team of heterogeneous autonomous underwater vehicles (AUV). The objective is to have the network of autonomous underwater vehicles follow a desired trajectory while agents maintain a desired formation. It is assumed that the model parameters associated with each vehicle is different although the order of the vehicles are the same. Three cooperative control schemes based on dynamic surface control (DSC) technique are developed. First, a DSC-based centralized scheme is presented in which there is a central controller that has access to information of all agents at the same time and designs the optimal solution for this cooperative problem. This scheme is used as a benchmark to evaluate the performance of other schemes developed in this thesis. Second, a DSC-based decentralized scheme is presented in which each agent designs its controller based on only its information and the information of its desired trajectory. In this scheme, there is no information exchange among the agents in the team. This scheme is also developed for the purpose of comparative studies. Third, two different semi-decentralized or distributed schemes for the network of heterogeneous autonomous underwater vehicles are proposed. These schemes are a synthesis of a consensus-based algorithm and the dynamic surface control technique with the difference that in one of them the desired trajectories of agents are used in the consensus algorithm while in the other the actual states of the agents are used. In the former scheme, the agents communicate their desired relative distances with the agents within their set of nearest neighbors and each agent determines its own control trajectory. In this semi-decentralized scheme, the velocity measurements of the virtual leader and all the followers are not required to reach the consensus formation. However, in the latter, agents communicate their relative distances and velocities with the agents within their set of nearest neighbors. In both semi-decentralized schemes only a subset of agents has access to information of a virtual leader. The comparative studies between these two semi-decentralized schemes are provided which show the superiority of the former semi-decentralized scheme over latter. Furthermore, to evaluate the efficiency of the proposed DSC-based semi-decentralized scheme with consensus algorithm using desired trajectories, a comparative study is performed between this scheme and three cooperative schemes of model-dependent coordinated tracking algorithm, namely the centralized, decentralized, and semi-decentralized schemes. Given that the dynamics of autonomous underwater vehicles are inevitably subjected to system faults, and in particular the actuator faults, to improve the performance of the network of agents, active fault-tolerant control strategies corresponding to the three developed schemes are also designed to recover the team from the loss-of-effectiveness in the actuators and to ensure that the closed-loop signals remain bounded and the team of heterogeneous autonomous underwater vehicles satisfy the overall design specifications and requirements. The results of this research can potentially be used in various marine applications such as underwater oil and gas pipeline inspection and repairing, monitoring oil and gas pipelines, detecting and preventing any oil and gas leakages. However, the applications of the proposed cooperative control and its fault-tolerant scheme are not limited to underwater formation path-tracking and can be applied to any other multi-vehicle systems that are characterized by Euler–Lagrange equations

Navigation and collision avoidance of underwater vehicles using sonar data

Collision avoidance is one of the main challenges in the field of autonomous underwater vehicles (AUV). In this paper a method for detecting obstacles is proposed, using a single-beam mechanically scanning sonar, including planning of an optimal path around the obstacles. Obstacle detection is achieved with an inverse-sonar model updating a vehicle-fixed occupancy grid. A new and obstacle-free path is planned using Voronoi diagrams and Dijkstras algorithm. The path is smoothed using Fermats spiral and a line of sight-guidance system with a time-varying lookahead-distance as guidance. The method is implemented and a full-scale test is performed from IKMs onshore control room on a remotely operated vehicle (ROV) operating at Statoils Snorre B oil field on the Norwegian Continental Shelf. The technology is applicable to ROVs and AUVs in underwater operations

Multi-stages robust autopilot for nonlinear command line of sight guided system

The performance of guided flight missile systems is measured through the minimum miss-distance and its capability to overcome target maneuver in presence of different sources of disturbances and noises. To achieve this objective, mathematical model for different dynamical behavior phases of command to the line of sight (CLOS) system is introduced using system identification technique. However, these mathematical models cannot precisely represent a real physical system without uncertainties. To improve system performance, a multi-stages robust autopilot is designed (each stage applied with corresponding missile flight phase) and compared with single stage robust autopilot. By using the H∞ approach, both controllers are obtained to adequate the nonlinear dynamical behavior and overcome different kind of uncertainties of the intended Command Line of Sight Guidance System (CLOS). Finally, the system performance and robustness are verified with flight path analysis by 6-DOF simulation model. © 2014 TCCT, CAA